Discovering motifs in DNA sequences

Authors:
J. W. Guan;D. Y. Liu;D. A. Bell
Affiliations:
College of Computer Science and Technology, Jilin University, 130012, Changchun, P.R.CHINA and School of Computer Science, The Queen's University of Belfast, Belfast, BT7 1NN, Northern Ireland, U. ...;College of Computer Science and Technology, Jilin University, 130012, Changchun, P.R.CHINA;School of Computer Science, The Queen's University of Belfast, Belfast, BT7 1NN, Northern Ireland, U.K.
Venue:
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Year:
2003

Citing 3
Cited 1

Computational methods for rough classification and discovery

Journal of the American Society for Information Science - Special issue: knowledge discovery and data mining
Rough computational methods for information systems

Artificial Intelligence
Mining Sequential Patterns: Generalizations and Performance Improvements

EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology

An Exact Data Mining Method for Finding Center Strings and All Their Instances

IEEE Transactions on Knowledge and Data Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Large collections of genomic information have been accumulated in recent years, and embedded latently in them is potentially significant knowledge for exploitation in medicine and in the pharmaceutical industry. The approach taken here to the distillation of such knowledge is to detect strings in DNA sequences which appear frequently, either within a given sequence (e.g., for a particular patient) or across sequences (e.g., from different patients sharing a particular medical diagnosis). Motifs are strings that occur very frequently.We present basic theory and algorithms for finding very frequent and common strings. Strings which are maximally frequent are of particular interest and, having discovered such motifs, we show briefly how to mine association rules by an existing rough sets based technique. Further work and applications are in progress.